import random
import copy
import sys
import tkinter
import threading
from functools import reduce

# 参数
"""
ALPHA:信息启发因子，值越大，则蚂蚁选择之前走过的路径可能性就越大
      ，值越小，则蚁群搜索范围就会减少，容易陷入局部最优
BETA:Beta值越大，蚁群越就容易选择局部较短路径，这时算法收敛速度会
     加快，但是随机性不高，容易得到局部的相对最优
"""
(ALPHA, BETA, RHO, Q) = (1.0, 2.0, 0.5, 100.0)
# 城市数，蚁群
(city_num, ant_num) = (50, 50)
distance_x = [
    178,
    272,
    176,
    171,
    650,
    499,
    267,
    703,
    408,
    437,
    491,
    74,
    532,
    416,
    626,
    42,
    271,
    359,
    163,
    508,
    229,
    576,
    147,
    560,
    35,
    714,
    757,
    517,
    64,
    314,
    675,
    690,
    391,
    628,
    87,
    240,
    705,
    699,
    258,
    428,
    614,
    36,
    360,
    482,
    666,
    597,
    209,
    201,
    492,
    294,
]
distance_y = [
    170,
    395,
    198,
    151,
    242,
    556,
    57,
    401,
    305,
    421,
    267,
    105,
    525,
    381,
    244,
    330,
    395,
    169,
    141,
    380,
    153,
    442,
    528,
    329,
    232,
    48,
    498,
    265,
    343,
    120,
    165,
    50,
    433,
    63,
    491,
    275,
    348,
    222,
    288,
    490,
    213,
    524,
    244,
    114,
    104,
    552,
    70,
    425,
    227,
    331,
]
import  numpy as  np

np.random.shuffle(distance_x)
np.random.shuffle(distance_y)
# 城市距离和信息素
distance_graph = [[0.0 for col in range(city_num)] for raw in range(city_num)]
pheromone_graph = [[1.0 for col in range(city_num)] for raw in range(city_num)]


# ----------- 蚂蚁 -----------
class Ant(object):
    # 初始化
    def __init__(self, ID):
        self.ID = ID  # ID
        self.__clean_data()  # 随机初始化出生点

    # 初始数据
    def __clean_data(self):
        self.path = []  # 当前蚂蚁的路径
        self.total_distance = 0.0  # 当前路径的总距离
        self.move_count = 0  # 移动次数
        self.current_city = -1  # 当前停留的城市
        self.open_table_city = [True for _ in range(city_num)]  # 探索城市的状态

        city_index = random.randint(0, city_num - 1)  # 随机初始出生点
        self.current_city = city_index
        self.path.append(city_index)
        self.open_table_city[city_index] = False
        self.move_count = 1

    # 选择下一个城市
    def __choice_next_city(self):
        next_city = -1
        select_citys_prob = [0.0 for _ in range(city_num)]  # 存储去下个城市的概率
        total_prob = 0.0

        # 获取去下一个城市的概率
        for i in range(city_num):
            if self.open_table_city[i]:
                try:
                    # 计算概率：与信息素浓度成正比，与距离成反比
                    select_citys_prob[i] = pow(
                        pheromone_graph[self.current_city][i], ALPHA
                    ) * pow((1.0 / distance_graph[self.current_city][i]), BETA)
                    total_prob += select_citys_prob[i]
                except ZeroDivisionError:
                    print(
                        "Ant ID: {ID}, current city: {current}, target city: {target}".format(
                            ID=self.ID, current=self.current_city, target=i
                        )
                    )
                    sys.exit(1)

        # 轮盘选择城市
        if total_prob > 0.0:
            # 产生一个随机概率,0.0-total_prob
            temp_prob = random.uniform(0.0, total_prob)
            for i in range(city_num):
                if self.open_table_city[i]:
                    # 轮次相减
                    temp_prob -= select_citys_prob[i]
                    if temp_prob < 0.0:
                        next_city = i
                        break

        # 未从概率产生，顺序选择一个未访问城市
        # if next_city == -1:
        #     for i in range(city_num):
        #         if self.open_table_city[i]:
        #             next_city = i
        #             break

        if next_city == -1:
            next_city = random.randint(0, city_num - 1)
            while (
                (self.open_table_city[next_city]) == False
            ):  # if==False,说明已经遍历过了
                next_city = random.randint(0, city_num - 1)

        # 返回下一个城市序号
        return next_city

    # 计算路径总距离
    def __cal_total_distance(self):
        global start
        temp_distance = 0.0

        for i in range(1, city_num):
            start, end = self.path[i], self.path[i - 1]
            temp_distance += distance_graph[start][end]

        # 回路
        end = self.path[0]
        temp_distance += distance_graph[start][end]
        self.total_distance = temp_distance

    # 移动操作
    def __move(self, next_city):
        self.path.append(next_city)
        self.open_table_city[next_city] = False
        self.total_distance += distance_graph[self.current_city][next_city]
        self.current_city = next_city
        self.move_count += 1

    # 搜索路径
    def search_path(self):
        # 初始化数据
        self.__clean_data()

        # 搜素路径，遍历完所有城市为止
        while self.move_count < city_num:
            # 移动到下一个城市
            next_city = self.__choice_next_city()
            self.__move(next_city)

        # 计算路径总长度
        self.__cal_total_distance()


class TSP(object):
    def __init__(self, root, width=800, height=600, n=city_num):
        # 创建画布
        self.root = root
        self.width = width
        self.height = height
        # 城市数目初始化为city_num
        self.n = n
        # tkinter.Canvas
        self.canvas = tkinter.Canvas(
            root,
            width=self.width,
            height=self.height,
            bg="#EBEBEB",  # 背景白色
            xscrollincrement=1,
            yscrollincrement=1,
        )
        self.canvas.pack(expand=tkinter.YES, fill=tkinter.BOTH)
        self.title("TSP蚁群算法(n:初始化 e:开始搜索 s:停止搜索 q:退出程序)")
        self.__r = 5
        self.__lock = threading.RLock()  # 线程锁

        self.__bindEvents()
        self.new()

        # 计算城市之间的距离
        for i in range(city_num):
            for j in range(city_num):
                temp_distance = pow((distance_x[i] - distance_x[j]), 2) + pow(
                    (distance_y[i] - distance_y[j]), 2
                )
                temp_distance = pow(temp_distance, 0.5)
                distance_graph[i][j] = float(int(temp_distance + 0.5))

    # 按键响应程序
    def __bindEvents(self):
        self.root.bind("q", self.quite)  # 退出程序
        self.root.bind("n", self.new)  # 初始化
        self.root.bind("e", self.search_path)  # 开始搜索
        self.root.bind("s", self.stop)  # 停止搜索

    # 更改标题
    def title(self, s):
        self.root.title(s)

    # 初始化
    def new(self, *args):
        # 停止线程
        self.__lock.acquire()  # 加锁
        self.__running = False
        self.__lock.release() # 解锁

        self.clear()  # 清除信息
        self.nodes = []  # 节点坐标
        self.nodes2 = []  # 节点对象
        np.random.shuffle(distance_x)
        np.random.shuffle(distance_y)
        # 初始化城市节点
        for i in range(len(distance_x)):
            # 在画布上随机初始坐标
            x = distance_x[i]
            y = distance_y[i]
            self.nodes.append((x, y))
            # 生成节点椭圆，半径为self.__r
            node = self.canvas.create_oval(
                x - self.__r,
                y - self.__r,
                x + self.__r,
                y + self.__r,
                fill="#ff0000",  # 填充红色
                outline="#000000",  # 轮廓白色
                tags="node",
            )
            self.nodes2.append(node)
            # 显示坐标
            self.canvas.create_text(
                x,
                y - 10,  # 使用create_text方法在坐标（302，77）处绘制文字
                text="(" + str(x) + "," + str(y) + ")",  # 所绘制文字的内容
                fill="black",  # 所绘制文字的颜色为灰色
            )

        # 顺序连接城市
        # self.line(range(city_num))

        # 初始城市之间的距离和信息素
        for i in range(city_num):
            for j in range(city_num):
                pheromone_graph[i][j] = 1.0

        self.ants = [Ant(ID) for ID in range(ant_num)]  # 初始蚁群
        self.best_ant = Ant(-1)  # 初始最优解
        self.best_ant.total_distance = 1 << 31  # 初始最大距离
        self.iter = 1  # 初始化迭代次数

    # 将节点按order顺序连线
    def line(self, order):
        # 删除原线
        self.canvas.delete("line")

        def line2(i1, i2):
            p1, p2 = self.nodes[i1], self.nodes[i2]
            self.canvas.create_line(p1, p2, fill="#000000", tags="line")
            return i2

        # order[-1]为初始值
        reduce(line2, order, order[-1])

    # 清除画布
    def clear(self):
        for item in self.canvas.find_all():
            self.canvas.delete(item)

    # 退出程序
    def quite(self, evt):
        self.__lock.acquire()
        self.__running = False
        self.__lock.release()
        self.root.destroy()
        print("\n程序已退出...")
        sys.exit()

    # 停止搜索
    def stop(self, evt):
        self.__lock.acquire()
        self.__running = False
        self.__lock.release()

    # 开始搜索
    def search_path(self, evt=None):
        # 开启线程
        self.__lock.acquire()
        self.__running = True
        self.__lock.release()
        last_best_path_count =0
        last_best_path =0
        while self.__running:
            # 遍历每一只蚂蚁
            for ant in self.ants:
                # 搜索一条路径
                ant.search_path()
                # 与当前最优蚂蚁比较
                if ant.total_distance < self.best_ant.total_distance:
                    # 更新最优解
                    self.best_ant = copy.deepcopy(ant)
            # 更新信息素
            self.__update_pheromone_gragh()
            print(
                "迭代次数：",
                self.iter,
                "最佳路径总距离：",
                int(self.best_ant.total_distance),
            )
            # 连线
            self.line(self.best_ant.path)
            # 设置标题
            self.title(
                "TSP蚁群算法(n:随机初始 e:开始搜索 s:停止搜索 q:退出程序) 迭代次数: %d"
                % self.iter
            )
            # 更新画布
            self.canvas.update()
            self.iter += 1

            if int(self.best_ant.total_distance) == last_best_path:
                last_best_path_count += 1
            if   last_best_path_count > 10:
                 self.__running=False
                 print("最佳路径总距离：", int(self.best_ant.total_distance))
            last_best_path = int(self.best_ant.total_distance)

    # 更新信息素
    def __update_pheromone_gragh(self):
        # 获取每只蚂蚁在其路径上留下的信息素
        temp_pheromone = [[0.0 for col in range(city_num)] for raw in range(city_num)]
        for ant in self.ants:
            for i in range(1, city_num):
                start, end = ant.path[i - 1], ant.path[i]
                # 在路径上的每两个相邻城市间留下信息素，与路径总距离反比
                temp_pheromone[start][end] += Q / ant.total_distance
                temp_pheromone[end][start] = temp_pheromone[start][end]

        # 更新所有城市之间的信息素，旧信息素衰减加上新迭代信息素
        for i in range(city_num):
            for j in range(city_num):
                pheromone_graph[i][j] = pheromone_graph[i][j] * RHO + temp_pheromone[i][j]

    # 主循环
    def mainloop(self):
        self.root.mainloop()


if __name__ == '__main__':

    TSP(tkinter.Tk()).mainloop()